Last week, while searching for information about deep learning distributed operation modes, I accidentally came across PaddlePaddle and found its distributed training program quite impressive. I wanted to share this with you. However, the content was too complex, so I decided to start with the first "hello world" example of PaddlePaddle—MNIST handwritten digit recognition. In the next article, I will introduce the distributed training approach using PaddlePaddle. In fact, I had previously written an article on recognizing handwritten digits using CNN with Keras. This time, after using PaddlePaddle, I can compare the strengths and weaknesses of both frameworks.
What is PaddlePaddle?
PaddlePaddle is a deep learning framework developed by Baidu. Most people are familiar with TensorFlow, Caffe, or MXNet, but PaddlePaddle is also a very powerful framework. It used to be called Paddle, and now it's renamed to PaddlePaddle. I find it a bit strange, but I'm still impressed by its capabilities.
What can PaddlePaddle do?
It can handle traditional tasks, especially in NLP, such as sentiment analysis, word embeddings, and language models. You can use it to experiment and build your own models easily.
How to install PaddlePaddle?
The official website mentions that the only officially supported way to run PaddlePaddle is via Docker, which isn't very popular in China. However, I tried installing it using pip, and it worked fine. For beginners, the easiest way to install is:
- CPU version: `pip install paddlepaddle`
- GPU version: `pip install paddlepaddle-gpu`
Handwritten Digit Recognition with PaddlePaddle
The training process includes importing data, defining the network structure, training the model, saving the model, and testing the results. Below is the code to demonstrate the training process (you can find the full code on GitHub):
```python
# coding:utf-8
import os
from PIL import Image
import numpy as np
import paddle.v2 as paddle
# Set whether to use GPU
with_gpu = os.getenv('WITH_GPU', '0') != '1'
# Define the network structure
def convolutional_neural_network(img):
conv_pool_1 = paddle.networks.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
num_channel=1,
pool_size=2,
pool_stride=2,
act=paddle.activation.Relu()
)
conv_pool_2 = paddle.networks.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
num_channel=20,
pool_size=2,
pool_stride=2,
act=paddle.activation.Relu()
)
predict = paddle.layer.fc(
input=conv_pool_2,
size=10,
act=paddle.activation.Softmax()
)
return predict
def main():
paddle.init(use_gpu=with_gpu, trainer_count=1)
images = paddle.layer.data(name='pixel', type=paddle.data_type.dense_vector(784))
label = paddle.layer.data(name='label', type=paddle.data_type.integer_value(10))
predict = convolutional_neural_network(images)
cost = paddle.layer.classification_cost(input=predict, label=label)
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128)
)
trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print("Pass %d, Batch %d, Cost %f, %s" % (event.pass_id, event.batch_id, event.cost, event.metrics))
if isinstance(event, paddle.event.EndPass):
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(paddle.dataset.mnist.test(), batch_size=128))
print("Test with Pass %d, Cost %f, %s" % (event.pass_id, result.cost, result.metrics))
trainer.train(
reader=paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=8192), batch_size=128),
event_handler=event_handler,
num_passes=10
)
if __name__ == '__main__':
main()
```
This code may look long, but it's well structured. Let’s test it with real data and see how it performs.
Baseline Version
I used the basic CNN structure provided by the official documentation. The output showed that the model achieved 98.79% accuracy in just five passes, which is quite good. The training time was around 31 seconds, which is much faster than using Keras.
Improved Version
To improve the model, I added dropout layers to prevent overfitting and introduced batch normalization. After these changes, the accuracy increased to 99.28%, and the training time was significantly reduced compared to Keras.
Summary
PaddlePaddle is very convenient to use, from defining the network structure to training speed. Some of its key advantages include:
1. Easy data import.
2. Customizable event handlers for detailed training outputs.
3. Fast performance, even with simple structures.
However, there are some downsides, such as limited documentation. Despite that, I believe PaddlePaddle is a great open-source tool, especially for distributed training. I plan to write more articles on practical applications and advanced features in the future.
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